Chapter 8 Differential abundance functional

8.1 Community level plots

accli_post2<-sample_metadata%>% 
  filter(time_point == "1_Acclimation"|time_point == "6_Post-FMT2")
accli_post2$newID<-paste(accli_post2$type, "_", accli_post2$time_point)

GIFTs_functions_community %>%
  as.data.frame() %>%
  rownames_to_column("sample") %>%
  left_join(accli_post2, by = join_by(sample == Tube_code)) %>%
  filter(time_point == "1_Acclimation"|time_point == "6_Post-FMT2") %>%
  select(c(1:29, 36,39)) %>%
  pivot_longer(-c(sample,type,time_point),names_to = "trait", values_to = "value") %>%
  mutate(trait = case_when(
    trait %in% GIFT_db3$Code_function ~ GIFT_db3$Function[match(trait, GIFT_db3$Code_function)],
    TRUE ~ trait
  )) %>%
  mutate(trait=factor(trait,levels=unique(GIFT_db3$Function))) %>%
  ggplot(aes(x=value, y=time_point, group=time_point, fill=type, color=type)) +
  geom_boxplot() +
  scale_color_manual(name="type",
                     breaks=c("Control","Hot_control", "Treatment"),
                     labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
                     values=c("#4477AA","#d57d2c","#76b183")) +
  scale_fill_manual(name="type",
                    breaks=c("Control","Hot_control", "Treatment"),
                    labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
                    values=c("#4477AA50","#d57d2c50","#76b18350")) +
  facet_grid(trait ~ type, space="free", scales="free") +
  theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1),
        strip.text.y = element_text(angle = 0)) + 
  labs(y="Traits",x="Metabolic capacity index")

GIFTs_elements_community_merged<-GIFTs_elements_community %>%
    as.data.frame() %>%
    rownames_to_column(var="sample") %>%
    filter(sample!="AD69") %>%
    pivot_longer(!sample,names_to="trait",values_to="gift") %>%
    left_join(sample_metadata, by = join_by(sample == Tube_code))%>%
    filter(time_point=="1_Acclimation"|time_point == "6_Post-FMT2")%>%
    mutate(functionid = substr(trait, 1, 3)) %>%
    mutate(trait = case_when(
      trait %in% GIFT_db3$Code_element ~ GIFT_db3$Element[match(trait, GIFT_db3$Code_element)],
      TRUE ~ trait
    )) %>%
    mutate(functionid = case_when(
      functionid %in% GIFT_db3$Code_function ~ GIFT_db3$Function[match(functionid, GIFT_db3$Code_function)],
      TRUE ~ functionid
    )) %>%
    mutate(trait=factor(trait,levels=unique(GIFT_db3$Element))) %>%
    mutate(functionid=factor(functionid,levels=unique(GIFT_db3$Function)))

# Create an interaction variable for time_point and sample
GIFTs_elements_community_merged$interaction_var <- interaction(GIFTs_elements_community_merged$sample, GIFTs_elements_community_merged$time_point)
  
ggplot(GIFTs_elements_community_merged,aes(x=interaction_var,y=trait,fill=gift)) +
        geom_tile(colour="white", linewidth=0.2)+
        scale_fill_gradientn(colours=rev(c("#d53e4f", "#f46d43", "#fdae61", "#fee08b", "#e6f598", "#abdda4", "#ddf1da")))+
        facet_grid(functionid ~ type, scales="free",space="free") +
        theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust=1, size=5),
              strip.text.y = element_text(angle = 0)) + 
        labs(y="Traits",x="Time_point",fill="GIFT")+
  scale_x_discrete(labels = function(x) gsub(".*\\.", "", x))

8.2 Wilcoxon comparison

8.2.1 Community elements differences: in CC acclimation vs post2

difference_table_CC %>% 
  filter(group_color=="Acclimation")

  Elements Acclimation      Post2                      Function     Element Difference group_color
1    T0503   0.1830714 0.01253516 Vitamin transport_Niacin (B3) Niacin (B3)  0.1705362 Acclimation

8.2.2 Community elements differences: in CI acclimation vs post2

difference_table_CI %>% 
  filter(group_color=="Acclimation")

8.2.3 Community elements differences: in WC acclimation vs post2

difference_table_WC %>% 
  filter(group_color=="Acclimation")

8.2.4 Comparison of both population in wild samples

difference_table_WC %>% 
  filter(group_color=="Acclimation")

  Elements Acclimation      Post2                              Function          Element Difference group_color
1    T0211  0.07898304 0.03960845           Amino acid transport_Serine           Serine 0.03937459 Acclimation
2    T0705  0.03026609 0.01792140      Nucleic acid transport_Allantoin        Allantoin 0.01234469 Acclimation
3    T0401  0.08571001 0.03355773    Organic anion transport_Spermidine       Spermidine 0.05215228 Acclimation
4    T0406  0.06660075 0.02039130     Organic anion transport_Succinate        Succinate 0.04620945 Acclimation
5    D0301  0.03445823 0.02175758             Sugar degradation_Lactose          Lactose 0.01270065 Acclimation
6    B0711  0.31689890 0.23431920 Vitamin biosynthesis_Menaquinone (K2) Menaquinone (K2) 0.08257970 Acclimation
7    T0503  0.05906234 0.02697581         Vitamin transport_Niacin (B3)      Niacin (B3) 0.03208653 Acclimation

####Butiryc acid biosynthesis

8.2.5 Comparison of both population in acclimation samples

8.2.6 Comparison of CC and CI in post2 samples

8.2.7 Comparison of CI and WC in post2 samples

8.2.8 Comparison of CI in accli and post1 samples

8.2.9 Comparison of CC in accli and post1 samples